A New Stereo Pair Disparity Index (SPDI) for Detecting Built-Up Areas from High-Resolution Stereo Imagery

A Mapping Review on Urban Landscape Factors of Dengue Retrieved from Earth Observation Data, GIS Techniques, and Survey Questionnaires

To date, there is no effective treatment to cure dengue fever, a mosquito-borne disease which has a major impact on human populations in tropical and sub-tropical regions. Although the characteristics of dengue infection are well known, factors associated with landscape are highly scale dependent in time and space, and therefore difficult to monitor. We propose here a mapping review based on 78 articles that study the relationships between landscape factors and urban dengue cases considering household, neighborhood and administrative levels. Landscape factors were retrieved from survey questionnaires, Geographic Information Systems (GIS), and remote sensing (RS) techniques. We structured these into groups composed of land cover, land use, and housing type and characteristics, as well as subgroups referring to construction material, urban typology, and infrastructure level. We mapped the co-occurrence networks associated with these factors, and analyzed their relevance according to a three-valued interpretation (positive, negative, non significant). From a methodological perspective, coupling RS and GIS techniques with field surveys including entomological observations should be systematically considered, as none digital land use or land cover variables appears to be an univocal determinant of dengue occurrences. Remote sensing urban mapping is however of interest to provide a geographical frame to distribute human population and movement in relation to their activities in the city, and as spatialized input variables for epidemiological and entomological models.

Line-Constrained Shape Feature for Building Change Detection in VHR Remote Sensing Imagery

Buildings represent the most relevant features of human activity in urban regions, but their change detection using very-high-resolution (VHR) remote sensing imagery is still a major challenge. Effective representation of the building is the key point in building change detection. The linear feature can indirectly represent the structure and distribution of man-made objects. Thus, this study proposes a shape feature-based building change detection method. Specifically, a line-constrained shape (LCS) feature is developed to capture the shape characteristics of buildings. This feature improves the discriminability between buildings and other ground objects by integrating the pixel shape feature and line segments. The building candidate area (BCA) is created in accordance with the distribution of the line segments in two-phase images. The problem space is constrained in a high-likelihood region of buildings because of the BCA. Comparative experimental results demonstrate that the combination of the spectral feature and the developed LCS feature achieves the best performance in object-based building change detection in VHR imagery.